3. Risk Management: Identify Project Exception Conditions Faster

Managing Project Risk is a critical responsibility of the Project Manager during each project to help quantify the impact of the risk on project schedule and costs, as well as respond with Project Risk Mitigation Plans.  Project Risk Identification and Control is also an opportunity area for implementing Artificial Intelligence (AI) and Machine Learning (ML) to analyze common factors that cause projects to run late and over budget, and alert the Project Manager. 

Artificial Intelligence (AI) improves Project Intelligence capabilities to monitor and assess Project exception conditions and alert Project Managers when intervention may be required.  Project Intelligence AI can monitor projects performance on a continuing basis to detect and report task-level issues before the Project reaches a critical threshold, which can lead to a schedule or budget overrun. 

Intelligent Project Alerts can notify Project Managers when Exception conditions occurring outside of the core project schedule, in areas like Purchasing and Assets.  For example, when project materials are received where quantities don’t match ordered quantities, or project materials are rejected during receiving.  While Machine Learning will guide project managers to create more accurate project schedules and estimates faster and easier by providing suggestions using ML from completed projects. 

Identification of Project Exceptions conditions to monitor Project Tasks that are running late, and generate Project alerts for exceptions which may have overall project schedule impacts.  Also, Project Exceptions Dashboards can provide an ideal reporting platform for AI/ML generated Task Alerts and Exceptions to Analyze impacts on the Project schedule, and respond accordingly to mitigate project risks.  

This capability can be used to identify which Project tasks will need attention, and build on Machine Learning capabilities for projects to recognize Project conditions where tasks run late, and may be under-estimated, missing the required materials/resources or other predecessor task dependencies.  

• Recognize Project conditions where tasks run late, and may be under-resourced, missing the required materials/resources, or missing predecessor Task dependencies.

• Recognize Project conditions where Supplier-related events are delayed, including delays to PO Approval, PO Releases, and Supplier Delivery delays.

• During PO Receiving, a Risk or Exception can be noted if the product/equipment received is moved to quarantine for inspection-related issues, or the quantity received doesn’t match PO.

• IOT Cloud Services can be used on projects for equipment geolocation, as well as monitoring major/engineered equipment with sensitive electronics/components that may be affected by severe vibrations/shocks (or humidity/moisture) as it is transported to Project sites.